Markerless Mobile Augmented Reality (AR) aims to anchor digital content in the physical world without using specific 2D or 3D objects. Absolute Pose Regressors (APR) are end-to-end machine learning solutions that infer the device's pose from a single monocular image. Thanks to their low computation cost, they can be directly executed on the constrained hardware of mobile AR devices. However, APR methods tend to yield significant inaccuracies for input images that are too distant from the training set. This paper introduces KS-APR, a pipeline that assesses the reliability of an estimated pose with minimal overhead by combining the inference results of the APR and the prior images in the training set. Mobile AR systems tend to rely upon visual-inertial odometry to track the relative pose of the device during the experience. As such, KS-APR favours reliability over frequency, discarding unreliable poses. This pipeline can integrate most existing APR methods to improve accuracy by filtering unreliable images with their pose estimates. We implement the pipeline on three types of APR models on indoor and outdoor datasets. The median error on position and orientation is reduced for all models, and the proportion of large errors is minimized across datasets. Our method enables state-of-the-art APRs such as DFNetdm to outperform single-image and sequential APR methods. These results demonstrate the scalability and effectiveness of KS-APR for visual localization tasks that do not require one-shot decisions.

翻译：无标记移动增强现实旨在无需特定二维或三维对象的情况下，将数字内容锚定于物理世界中。绝对位姿回归器是一种端到端的机器学习解决方案，可从单目图像推断设备位姿。凭借其低计算成本，它们可直接在移动增强现实设备的受限硬件上执行。然而，对于与训练集差异过大的输入图像，APR方法往往产生显著误差。本文提出KS-APR流水线，通过结合APR推理结果与训练集中的先验图像，以最小开销评估估计位姿的可靠性。移动AR系统通常依赖视觉-惯性里程计在体验过程中追踪设备的相对位姿。因此，KS-APR优先考虑可靠性而非频率，丢弃不可靠位姿。该流水线可集成大多数现有APR方法，通过过滤带位姿估计的不可靠图像来提升精度。我们在室内外数据集上对三种类型的APR模型实现了该流水线。所有模型的位置和方向中位误差均有所降低，且各数据集中大误差比例被最小化。我们的方法使DFNetdm等最先进APR优于单图像及序列APR方法。这些结果证明了KS-APR在无需一次性决策的视觉定位任务中的可扩展性和有效性。